In forensic analysis, semiconductor defect review, gunshot residue analysis and general materials analysis of alloys, ceramics, glasses, minerals, polymers and powders, the identity of a fragment of material can often be determined by collecting and analysing an x-ray energy spectrum. The x-rays can be excited from the fragment for example by a beam of x-rays (x-ray fluorescence, XRF) or electrons (electron probe microanalysis, EPMA) or protons (proton induced x-ray emission, PIXE). X-rays are often detected by an energy dispersive x-ray detector (EDX) which is typically a lithium drifted Si, Si(Li), detector or a silicon drift detector, SDD. For example, in U.S. Pat. No. 6,326,619, energy-dispersive x-ray (EDX) analysis is used in a scanning electron microscope (SEM) to find a list of major elements present in the sample and this list is used to filter and select only those entries in the crystallographic database that have at least these elements. This narrows down the number of candidates that need to have their crystallography compared by EBSD pattern analysis.
In U.S. Pat. No. 6,835,931 EDX analysis is used to provide a “chemical filter”. The amounts of each element found by analysis of the EDX spectrum are compared with upper and lower limits for each of the phases in a list of phases that may be present in the region of interest and the crystallographic information obtained by EBSD then compared with that stored with the list of phases to find out which crystallographic phase is present.
In both these methods, some measure of the amount of each element present in the sample is required to decide which entries in a database are appropriate, thus providing a filter for those entries. An alternative approach is to use “spectral fingerprinting” to find out what material has a composition closest to the sample being analysed. Some examples of this approach include JP-A-108253/1988, US2004/0099805, U.S. Pat. No. 6,519,315 and U.S. Pat. No. 6,140,643.
When an x-ray spectrum is measured by for example EDX, sharp peaks appear in the spectrum for each chemical element in the sample corresponding to the characteristic line emissions for that element and a broad background appears due to continuous physical effects within the sample. To obtain the amounts of each element present, mathematical processing of the digitised energy spectrum is required to correct for the continuous background and resolve any overlapping peaks from different elements. Errors in background subtraction, slight miscalibration of the spectrometer or incorrect characterisation of peak shape can result in inaccurate estimates of peak areas. Furthermore, to convert the areas into mass concentration for elements requires x-ray correction procedures that may introduce additional sources of inaccuracy. Even if two chemical elements are present in the sample at the same mass concentration level, the efficiency of x-ray production may sometimes differ so that the characteristic peaks have areas that may be different by orders of magnitude. Some characteristic radiation at low energy may be heavily absorbed on exit from the sample and by materials within the detector entrance window so that the peak may not be detectable in the spectrum.
The source of excitation, whether electron beam or x-ray beam, defines the maximum energy of the emitted radiation from the sample and if the maximum energy in the source is too low, then some characteristic lines at higher energy may not be excited. If the total counts recorded in the x-ray spectrum is too low, then the statistical precision in background subtraction may be insufficient to reliably detect very low intensity characteristic peaks. If the source of excitation scatters off the sample and strikes material other than the sample being analysed, including material within the detector entrance window, then spurious peaks appear in the spectrum. These spurious peaks may be misidentified as elements from within the sample. Even if these peaks are small, the apparent mass concentration for the misidentified element may be large after the peak area is corrected for excitation efficiency and any anticipated effects of absorption.
As a consequence, x-ray spectral analysis may sometimes fail to detect certain elements present in the sample, may detect elements which are not present in the sample (false positives) and may give concentration estimates which are inaccurate. When elemental compositional analysis is used as a filter to select candidates from a large database, tolerance limits can be relaxed to allow for errors in concentration and a subset of detectable elements can be used to avoid the problems of missing elements. However, the occurrence of false positives from spectral analysis can exclude the correct candidate within the database. Even if there are no false positives, relaxing the tolerance on upper and lower concentration limits may allow far too many candidates to be allowed through so that the filter is not effective in restricting the choice of likely material for the sample.
“Spectrum matching”, provides a means of overcoming some of the difficulties of the filtering. In this approach, some metric is evaluated for the unknown and a reference spectrum from a known material. By evaluating the metric for a large number of reference spectra of known materials and ranking the results in order of increasing value, candidate materials are provided in order of similarity to the unknown. Spurious peaks that would cause a filtering approach to fail completely will usually only provide a small change to the metric so even if the reference spectrum for the correct material does not give the best match, the metric will usually put it in the top few best matches. Thus selection of the correct material by other techniques is made easier. This “spectrum matching”, like fuzzy logic approaches, avoids the sharp exclusion properties of a filter while offering some tolerance of errors and some assistance in choosing between similar candidate materials.
The problem with the spectrum matching approach is that reference spectra need to be acquired for all candidate materials and structures under similar conditions to that used to acquire the spectrum from the unknown sample. This limits the practical number of materials that can be identified and the conditions that can be used for analysis. Furthermore, the spectrometer used to measure spectra from the reference materials usually has to have similar resolution and efficiency to the spectrometer used for measuring the unknown sample.